[PDF] Top 20 Hierarchical aesthetic quality assessment using deep convolutional neural networks
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Hierarchical aesthetic quality assessment using deep convolutional neural networks
... the aesthetic quality and assigning an aesthetic score are challenging ...the aesthetic quality of ...associated convolutional neural network (CNN) which learns the ... See full document
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Ensemble of Deep Convolutional Neural Networks for Classification of Early Barrett's Neoplasia Using Volumetric Laser Endomicroscopy
... features using several machine learning methods, such as support vector machine, random forest or AdaBoost, and showed successful results towards BE neoplasia ... See full document
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Transparent text quality assessment with convolutional neural networks
... on deep convo- lutional neural networks, which is able to estimate text quality at both the local and global scale, al- lowing easy visualization of weak or strong points of the ...is ... See full document
5
Individual minke whale recognition using deep learning convolutional neural networks
... The only known predictable aggregation of dwarf minke whales ( Balaenoptera acutorostrata subsp . ) occurs in the Australian offshore waters of the northern Great Barrier Reef in May-August each year. The identification ... See full document
12
Disease Detection of Plants using Deep Learning and Convolutional Neural Networks
... by Convolutional Neural Networks, which is a type of Deep Neural Network which was developed as similar as Human Visual system, Many CNN models was proposed to use it for recognising ... See full document
5
Detection of Sarcasm in Text Data using Deep Convolutional Neural Networks
... 2. Sentiment Analysis and Opinion Mining. One thing that every person wants know that ”What other people think” to help them in a decision making process. Many years before when we were at midst of World Wide Web, we ... See full document
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A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks
... Pre-processing A two-step methodology has been employed in filtering the datasets used in our ex- periments. Firstly, we identified and removed all the “user”, “URL” and “hashtag” references present in the tweets ... See full document
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Vision based human action recognition using machine learning techniques
... In recent years, various methods have been published for multiview action recognition, but very few are actually suitable for real-time applications due to their high computa- tional cost. Therefore, the cost of the ... See full document
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Deep Learning for Detecting Building Defects Using Convolutional Neural Networks
... towards an automated detection and localisation of key building defects, e.g., mould, deterioration, 18.. and stain, from images.[r] ... See full document
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The Application of Neural Network in Multiple Object Tracking
... of neural networks in multiple object tracking is the main reasons are as ...Firstly, neural networks usually need a huge number of training data to learn the large parameters, but we can only ... See full document
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Nikolov Radivchev at SemEval 2019 Task 6: Offensive Tweet Classification with BERT and Ensembles
... when using higher dimensional vectors, using 200-dimensional vectors proved to have no significant advantage in achieved results over 100-dimensional ones, and proved to be more computationally expensive, ... See full document
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Hierarchical Classification with Convolutional Neural Networks for Biomedical Literature
... for deep models called Deep Belief ...basic deep learning methods to represent the document information, which had a better result than LDA ...document, Convolutional Neural Network ... See full document
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YNUWB at SemEval 2019 Task 6: K max pooling CNN with average meta embedding for identifying offensive language
... al neural network model based on word2vec em- bedding(Gamb¨ack and Sikdar, ...for deep neural networks based on convo- lution and gated recursive networks was proposed by Zhang et ... See full document
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Combination of Multiple Acoustic Models with Multi-scale Features for Myanmar Speech Recognition
... The bias b (ℓ) is added and finally the activation function σ(.), typically sigmoid or ReLU will be applied to generate the outputs of the convolutional layer. When multiple feature maps are presented in the ... See full document
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Applying deep matching networks to Chinese medical question answering: a study and a dataset
... We filtered the questions that have adopted answers among all the collected data, which add up to a total of 65941 pieces. Then we cleaned up all the web tags, links, and garbled bytes leaving only digits, punctuations, ... See full document
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Modeling Interestingness with Deep Neural Networks
... lated deep neural networks to computer vision (Krizhevshy et ...a deep neural network to map documents to feature vec- tors in a latent semantic ...the deep models used for ... See full document
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Deep Pyramid Convolutional Neural Networks for Text Categorization
... els are word-level and therefore use the knowledge of word boundaries which character-level mod- els have no access to. While this is arguably not an apple-to-apple comparison, since word bound- aries can be obtained for ... See full document
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Spam detection in im images using convolutional neural networks
... we check our WhatsApp, we are usually bombarded with many messages. Some of them are from our loved ones, while others are of professional importance. But unfortunately, among these messages are many unwanted messages, ... See full document
6
Deep learning for smart agriculture: Concepts, tools, applications, and opportunities
... years, Deep Learning (DL), such as the algorithms of Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Generative Adversarial Networks (GAN), has ... See full document
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Deep Learning: Approaches and Challenges
... popular deep learning tools and libraries that are available to construct and execute efficiently deep learning ...Environment, deep learning toolk- its provide a development environment that makes ... See full document
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